Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Query flocks: a generalization of association-rule mining
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Integrating association rule mining with relational database systems: alternatives and implications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Optimization of constrained frequent set queries with 2-variable constraints
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Association Rule Mining in Peer-to-Peer Systems
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Distributed methodology of cantree construction
MIWAI'11 Proceedings of the 5th international conference on Multi-Disciplinary Trends in Artificial Intelligence
Topic model for analyzing purchase data with price information
Data Mining and Knowledge Discovery
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The advent of data warehouses has shifted the focus of data mining from file-based systems to database systems in recent years. Architectures and techniques for optimizing mining algorithms for relational as well as Object-relational databases are being explored with a view to tightly integrate mining into data warehouses. Interactive mining and incremental mining are other useful techniques to enhance the utility of mining and to support goal oriented mining. In this paper, we show that by viewing the negative border concept as a constraint relaxation technique, incremental data mining can be readily generalized to efficiently mine association rules with various types of constraints. In the general approach, incremental mining can be viewed as a special case of relaxing the frequency constraint. We show how the generalized incremental mining approach including constraint handling can be implemented using SQL. We develop performance optimizations for the SQL-based incremental mining and present some promising performance results. Finally, we demonstrate the applicability of the proposed approach to several other data mining problems in the literature.